| Challenge: | Existing dialogue systems focus on training a holistic response generation model without any distinction between different initiatives. |
| Approach: | They propose a general mix-Initiative Dynamic Prefix Tuning framework to decouple different initiatives from the generation model. |
| Outcome: | The proposed framework outperforms baselines on two public dialogue datasets on human evaluations and automatic metrics. |
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Controllable Mixed-Initiative Dialogue Generation through Prompting (2023.acl-short)
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| Challenge: | Mixed initiative dialogue systems allow all interacting agents to initiate actions to control the interaction. |
| Approach: | They propose to prompt large language models as a drop-in replacement for fine-tuning on conditional generation. |
| Outcome: | The proposed prompts improve fine-tuning and ground truth responses . the results show that generated responses are high . |
Harmonizing Code-mixed Conversations: Personality-assisted Code-mixed Response Generation in Dialogues (2024.findings-eacl)
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| Challenge: | blending multiple languages within a single conversation presents a formidable challenge, given the wide-ranging variations influenced by individual speaking styles and cultural backgrounds. |
| Approach: | They propose a novel approach to harness the Big Five personality traits acquired in an unsupervised manner from code-mixed conversations to bolster the performance of response generation. |
| Outcome: | The proposed approach enhances contextual relevance and performance of the proposed model by combining personality traits with dialogue context. |
IAPT: Instance-Aware Prompt Tuning for Large Language Models (2024.acl-long)
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| Challenge: | Existing methods for prompt tuning require many soft tokens to guarantee performance . large language models still require a large amount of GPU memory and computations to fine-tune . |
| Approach: | They propose to use a parameter-efficient soft prompt generator to generate idiosyncratic soft prompts for each input instruction. |
| Outcome: | The proposed method outperforms the baselines with comparable tunable parameters and is more efficient than LoRA under the single-backbone multi-tenant setting. |
Focused Prefix Tuning for Controllable Text Generation (2023.acl-short)
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| Challenge: | Existing unannotated attributes could degrade models' performance . focus on the desired attribute can be achieved with focused prefix tuning . |
| Approach: | They propose focused prefix tuning to enable the control to focus on the desired attribute . they propose to reduce the number of unannotated attributes in a controllable text generation dataset . |
| Outcome: | The proposed approach achieves better control accuracy and text fluency than baseline models in single-attribute tasks. |
Prefix-Tuning: Optimizing Continuous Prompts for Generation (2021.acl-long)
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| Challenge: | Fine-tuning is the prevalent paradigm for using large pretrained language models for downstream tasks, but it requires updating and storing all the parameters of the LM. |
| Approach: | They propose a lightweight alternative to fine-tuning for natural language generation tasks that optimizes a sequence of continuous vectors, which they call the prefix. |
| Outcome: | The proposed approach outperforms fine-tuning in the full data setting and extrapolates better to examples with topics that are unseen during training. |
Discourse-Aware Soft Prompting for Text Generation (2022.emnlp-main)
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| Challenge: | Recent advances in pre-trained langauge models (PLMs) have made great impact on text generation research. |
| Approach: | They propose to use hierarchical blocking to simulate a higher-level discourse structure of human written text and attention sparsity to learn sparse transformations on the softmax-function. |
| Outcome: | The proposed methods perform better on some generation tasks but don't generalize across all generation tasks. |
PREADD: Prefix-Adaptive Decoding for Controlled Text Generation (2023.findings-acl)
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| Challenge: | Existing methods for controlling text generation rely on auxiliary expert models but require training data. |
| Approach: | They propose a flexible method for controlled text generation that uses a prefix-prepended prompt instead of an external model. |
| Outcome: | The proposed method outperforms auxiliary-expert methods on three tasks by 12% or more on main metrics for each task. |
ATTEMPT: Parameter-Efficient Multi-task Tuning via Attentional Mixtures of Soft Prompts (2022.emnlp-main)
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| Challenge: | a new multi-task, parameter-efficient language model tuning method learns to transfer knowledge across different tasks via a mixture of soft prompts. |
| Approach: | They propose a multi-task, parameter-efficient language model tuning method that uses soft prompts to learn to transfer knowledge across different tasks. |
| Outcome: | The proposed method outperforms prompt tuning and outperfies or matches fully fine-tuned tuning approaches that use 10 times more parameters. |
Contextual Dynamic Prompting for Response Generation in Task-oriented Dialog Systems (2023.eacl-main)
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| Challenge: | Existing studies show that large pre-trained language models can be adapted to task-oriented dialog systems. |
| Approach: | They propose to use contextual dynamic prompting to generate prompts in dialogs . they propose to distill useful prompting signals from dialog contexts based on contextual dynamic . |
| Outcome: | The proposed approach improves response generation by 3 points and 17 points when dialog states are incorporated. |
Dynamic Prefix-Tuning for Generative Template-based Event Extraction (2022.acl-long)
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| Challenge: | Experimental results show that our model achieves competitive results with the state-of-the-art classification-based model OneIE on ACE 2005. |
| Approach: | They propose a generative template-based event extraction method with dynamic prefix . they integrate context information with type-specific prefixes to learn a context-specific name for each context . |
| Outcome: | The proposed method achieves competitive results with state-of-the-art model OneIE on ACE 2005 and performs well on ERE. |